Motivation

Principal goals of thesis:

1. identify a material issue, and
2. address it with a tangible solution

Motivation

• Companies can highlight their brand via R packages i.e. ggtech (Airbnb, Facebook, Google), ggthemes (The Economist, The Wall Street Journal)
• Duke has over 17,000 students, 38,000 employees, six campuses and spends more than $1 billion annually in research expenditures
• No existing R package to represent the Duke brand in data visualization

Motivation

• Data visualization industry expected to nearly double by 2026

• Important questions to consider:

  • Who is our audience?
  • How can we make our visualizations more intelligible?
  • Who might we be leaving behind?

• To reach largest audience, Duke branding package must consider colorblind accessibility

Background

Perspective Comparison

Background

• Duke provides resources on how their color suite meets the Web Content Accessibility Guidelines (WCAG)
  • WCAG: globally recognized standard for web accessibility best practices in all fields
• No pathway for users to integrate accessibility information into their visualizations
• The duke package aims to fill this unique branding and accessibility need

Color Resources

• Duke official color palette
  • Intended to bolster the brand of the University and ensure cohesion among all official content
• 2 primary colors

• 18 secondary and tertiary colors

Color Difference Metrics

• Metrics using the standard RGB (sRGB) space can be flawed due to its perceptual irregularity
• CIE chromaticity diagram exhibits the color spectrum according to human vision

Color Contrast Ratio

Accessibility Metrics

Accessibility guide ranks color pairing using following categories:

• AAA: highest compliance, text-background contrast ratio of 7+
• AA: mid-range compliance, text-background contrast ratio of 4.5+
• AA18: mid-range compliance with large text only (18pt or 14pt bold), text-background contrast ratio of 3+
• DNP: does not pass compliance guidelines, text-background contrast ratio of less than 3

Definition of Optimal Pallette

Ad-hoc approach in order to strike a balance between accessibility and recognition of the Duke brand.

theme_duke()

• Sets the aesthetic elements for the visualization’s background and accessory features (background color, grid lines, axis ticks, plot text, etc.)
• Minimalist design with a White background and grid lines removed
• Text in Duke Navy Blue for strong contrast to the White background (CCR of 14.76)
• Relative text sizing for consistent scale

Font specification in theme_duke()

• Font specification is challenging
• Recommend the use of Atkinson Hyperlegible font
  • “Focuses on letterform distinction to increase character recognition” (Staff, n.d.)
• Optimal solution would default to use of Atkinson Hyperlegible
  • Stability across systems and systemfonts issues are prohibitive

theme_duke()

scatterplot

scatterplot +
  theme_duke()

scale_duke_continuous()

• Applies a Duke-specific continuous color scale to geometric objects (geoms)
• Scale sequences from a dark (Duke Royal Blue) to a light color (Hatteras)
  • Duke Royal Blue and White: 7.75
  • Hatteras and White: 1.25
• Continuous color scales used for overall trend recognition

All pairings

Selected pairings

Discrete Palette

• 14 of 28 color pairings are compliant with WCAG AA18 rating.
• Color used for fill or color of geometric object, which offers complimentary features (ie. shape, larger areas, borders).
• Best practice to use three to five colors from discrete scale (Wilke, n.d.)

scale_duke_color_discrete()

splot +
  theme_duke() +
  scale_duke_color_discrete()
comp

scale_duke_fill_discrete()

bplot +
  theme_duke() +
  scale_duke_fill_discrete()
bcomp

Lessons Learned

Data

• Sourced from the Office of the University Registrar
• Data on all Duke courses between 2013-2022
  • 10 variables, 63,433 observations
• Registrar supporting students, faculty and staff in enrollment, academic records, and course scheduling

Number of Courses By Department

With this information, the Registrar can better make decisions about the distribution of University resources and/or understand potential department popularity.

Average Class Size

Helpful resource for the Registrar to pinpoint departments that would benefit from reducing class size.

Enrollment in Computer Science Courses

Visualizations suggest that students do not enroll at a high enough rate to fill Computer Science courses.

Enrollment in Introductory Courses

May inform the Registrar’s process of 1) adding sections to these courses and/or 2) identifying reasons for these courses’ success.

Seminar courses

If the University aims to improve their overall seminar offerings, it is important that they identify departments with lowest proportions.

1. Keep It Simple

2. De-Emphasize the Need for Color

- “Get it right in black and white” (Muth 2020)

- Use labels, arrows, shapes, annotations to differentiate between categories

Scatterplot using shape

Line plot using different linetypes

Bar plot with fill texture

3. Adjust When Necessary

Limitations + Future Work

duke package